Numerous companies provide recommendation engines for items ranging from shoes to movies. Most recommendation engines use one of two approaches. In one approach, an expert rates the object. For example, movie reviews are an example of an expert driven system. Users can then see what the expert thought of the movie. In one embodiment, the prior art recommendation system may recommend objects to a user based on correlation between a user's past history (or activities) and the expert ratings of the various objects in the system.
An alternative approach is to collect ratings data from users. For example, Netflix™ allows users to rate movies on a 1-5 star scale. The system then correlates the user's ratings with other user's ratings, to predict a user's rating/preference on a movie based on other similar users in the system.